In the era of Information Technology and Artificial Intelligence machine learning has become a tool to see the world and process visual data. Machine learning has taken the position of the most in-demand job in today’s world and a machine learning engineer nowadays is earning a handsome salary.
Machine learning algorithms are making computers smarter day by day. With the help of these algorithms computers, today are playing chess, performing surgeries, driving cars, etc. If someone has basic computer knowledge and wants to change careers for a better future, machine learning is a good option.
For that matter, if you are just a machine learning enthusiast and have the curiosity to understand machine learning the world of computer algorithms can be an enchanting and rewarding passion.
We have selected the best top 20 machine learning books catering to beginners or adding a book to the library of an advanced data science learner.
Author: John Paul Mueller and Luca MassaronFormat: Kindle EditionPrint length: 316Publisher: For dummiesEdition: 1st
This book helps you not only get a clear picture of technology but also addresses some major myths about it. It gives a very engrossing perspective about the application of technology in everyday life be it, self-driven cars, or its achievements in the field of medicine.
Discover the history of Artificial Intelligence.Understanding the role of data.Role of Artificial Intelligence in computer applications, Medicines, Machine Learning, etc.Clearing the misconceptions about AI.Exploration about drones and robots.
John Paul Mueller is the author of 108 books and more than 600 articles covering topics like AI, Networking, and Data Base Management. He is a technical editor and consultant by profession.
Luca Massaron is a specialist in multivariate statistical analysis, machine learning, and customer insight. Professionally he is a data scientist and marketing, research director.
Author: RusselFormat: KindlePrint Length: 1136Publisher: PearsonEdition: 4th
Around the world, many faculties of different universities recommend this book to students who are beginners and are just entering the world of artificial intelligence. This book gives a detailed insight into the field of AI and related research topics. This book also provides helpful references for further study. As it is a very detailed book that is why it cannot be read quickly especially when we want to have a sound command on the topic.
IntroductionIntelligent AgentsSearching to solve problemsKnowledge and ReasoningUncertain knowledge and reasoningLearningCommunicating, Perceiving, and ActingConclusions
Stuart Russel is a professor of computer science at the University of California, Berkeley, and adjunct professor of neurological surgery at the University of California, San Francisco. He is a computer scientist and 1400 universities in 128 countries recommend his book Artificial Intelligence a Modern Approach.
Author: Max TegmarkFormat: Kindle EditionPrint Length: 384Publisher: PenguinEdition: 1st
This book encompasses the tremendous development in the field of artificial intelligence and its potential to turnaround the future of mankind more than any other form of technology. This book also discusses the point of view on some controversial topics like consciousness and eventual physical limits on life in the solar system.
Life 1.0 (Biological stage): Evolving its hardware and software.
Life 2.0 (Cultural stage): Evolving its hardware and designing most of its software.
Life 3.0 (Technical stage): Designing its hardware and software.
An MIT professor Max Tegmark is the author of two books and more than 200 technical papers. The topics range from Cosmology to Artificial Intelligence. His unorthodox ideas and love for adventure has got him the name of “Mad Max”
Author: Tom M. MitchellFormat: TextbookPublisher: McGraw Hill EducationPrint length: 432Edition: 1st
This book is a detailed study of Machine learning algorithms and theorems. It comprises detailed examples with case studies to help the reader in having precise knowledge about machine learning algorithms. Anyone aiming to start a career in machine learning, this book will prove to be the ultimate guide for a beginner.
Genetic Algorithms.Inductive logic programming.Introduction to primary approaches regarding machine language.Concepts and techniques of Machin learning.Re-enforcement learning.
Tom M. Mitchell is a university professor at Carnegie Mellon University. His contribution to the advancement of machine learning, artificial intelligence, and cognitive neuroscience is phenomenal.
Author: Ian Goodfellow, Yoshua Bengio, and Aaron CourvilleFormat: KindlePublisher: MIT pressPages: 800Edition: 1st
According to Elon Musk, Deep Learning is a comprehensive and complete book on the topic. From the start of the decade, deep learning has become a steppingstone in the world of technology. This book has the basic concepts, practical aspects, and topics related to advanced research which make it a hands-on guidebook not only for the learners and practitioner but instructors as well.
Introduction.Part I: Applied Math and Machine Learning Basic.Part II: Modern Practical Deep NetworkPart III: Deep Learning Research
Ian Goodfellow is serving as a research scientist at Google.
Yoshua Bengio, at the Université de Montréal is a professor of computer science.
Aaron Courville, at the Université de Montréal is an assistant professor of computer science.
Author: Joel GrusFormat: Kindle Edition/PaperbackPublisher: O’ ReillyPages: 500Edition: 2nd
Math and science are the core of data science. This book will guide you to learn them as well as hacking skills which are very much required to be a data scientist. This book also helps you to explore the natural processing of language and the analysis of the network.
Implement k-nearest neighbors.Naïve Bayes.Linear and Logistic Regression.Decision Trees.Clustering Models.
Joel Grus is a software engineer by profession and is working at google.com. He has experience working as a data scientist at various startups. He is a regular in attending data science happy hours.
Author: Christopher M. BishopFormat: Hardcover/Kindle/PaperbackPublisher: SpringerPages: 738Edition: 2nd
This book is the first of its kind which gives a graphical model on machine learning. This book provides comparative inference algorithms that give quick answers to situations where clear-cut answers are not possible. To be able to grasp all the concepts presented in the book you need to have a basic, understanding of Multivariate Calculus and Basic Linear Algebra.
Approximate inference algorithms.Bayesian Methods.Introduction to basic probability theory.Introduction to pattern recognition and Machine learning.New Models based on Kernels.
Chris Bishop is a Microsoft scientist and Laboratory Director at Microsoft Research Cambridge. He is also serving as a professor of computer science at the University Of Edinburgh and a Fellow of Darwin College Cambridge.
Author: Oliver TheobaldFormat: Kindle/PaperbackPublisher: Scatterplot PressPages: 164Edition: 2nd
If you are looking for a book that is neither long nor has complex language, then this book is a must-read for you. Although plain English is used this book covers all the topics related to high-level introduction to Machine Learning in a practical and beginner-friendly way.
IntroductionWhat is Machine LearningML Categories?The ML ToolboxData ScrubbingSetting Up Your DataRegression AnalysisClusteringBias & VarianceArtificial Neural NetworksDecision TreesEnsemble ModelingBuilding A Model In PythonModel OptimizationFurther ResourcesDownloading DatasetsFinal Word
Oliver Theobald has enjoyed Best Seller Status on Amazon. His book Machine Learning for Absolute Beginners has been adopted by many universities. He has a background in technical writing/documentation and operations using AI and cloud computing. Recently he is into BI (Business Intelligence).
Author: Tariq RasheedFormat: Kindle/PaperbackPublisher: Create space for independent publishingPages: 222Edition: 2nd
Deep Learning and artificial intelligence both have a key element of neural networks. This guidebook gives you an understanding of neural networks in a simple yet insightful manner. Simple knowledge of secondary school mathematics will make the understanding of neural networks easy and coding in python become accessible.
Introduction to mathematical ideas underlining the neural networks.Python programming language and neural network buildup.Performance of neural networks and get is all working on a Raspberry P
The author is a physics degree holder with a Master’s in Machine Learning and Data Mining. London Python meet-up group is lead by him.
Author: Leonard EdisonFormat: PaperbackPublisher: Create space independent publishing platformPages: 292Edition: 1st
After completing this book, you will be equipped to use python for writing simple codes. It will also direct you in the right direction. Once you have gone beyond the beginner level in python.
Basics of Artificial Intelligence.Some of the branches of artificial intelligence.Decision Trees.Basic Python programming language.Logistic Regression
Leonard Edison is a computer science teacher and who writes blogs as well. For the past some years he is using his experience in this field to write books to pass on his knowledge to the readers.
Authors: David J. C. MacKayFormat: Kindle/Hard Cover/PaperbackPublisher: Cambridge University PressPages: 640Edition: 1st
This book was published almost 20 years ago but its relevance cannot be denied even today. It has a multi-disciplinary approach to establish connections between information theory learning algorithms and inference. It does not give its reader a lot of practical examples, but it serves its purpose as an introductory book for beginners.
Data CompressionNoisy-channel codingFurther Topic in Information TheoryProbabilities and InferenceNeural NetworksSparse Graph Codes
David J. C. MacKay was the Regius professor of engineering in the department of engineering at the University of Cambridge. He also served as Chief Scientific Advisor at the Department of Energy and Climate Change in the UK.
Author: Jeff HeatonFormat: Kindle/PaperbackPublisher: CreateSpace Independent Publishing PlatformPages: 224 Edition: 1st
In this book, algorithms are explained with the help of actual numeric calculations which can be performed by the reader himself. This is specially designed to cater to those people who learn AI but do not have a thorough mathematical knowledge.
Basic Algorithms of Artificial IntelligenceDimensionalityDistance MetricsClusteringError CalculationHill ClimbingNelder MeadLinear Regression
Jeff Heaton is professionally a computer scientist with a specialization in Python, R, Java, and C#. Jeff has a master’s degree in Information Management and Ph.D. in Computer Science. He has authored more than 10 books.
Author: Charu C. AggarwalFormat: E-Textbook/Hard Cover/PaperbackPublisher: SpringerPages: 520Edition: 1st
This book explains deep learning with classical and modern models. Neural Networks, their theory, and algorithms have been discussed in this book in great detail. This book explains Machine Learning through Neural Networks and the theory behind them.
The Basics of Neural NetworksFundamentals of Neural NetworksAdvanced Topics in Neural Networks
Charu C. Aggarwal is a DRSM (Distinguished Research Staff Member) at IBM at the Watson Research Center in Yorktown Height, NY. In 1993 he acquired his undergraduate degree in Computer Science from IIT at Kanpur and his Ph.D. from MIT in 1996. He has exhaustive experience in the Data Mining field.
Author: Aurélien GéronFormat: Kindle/PaperbackPublisher: O’ReillyPages: 600Edition: 2nd
Deep intuitive understanding regarding the concepts and rules to build intelligent systems can be learned through this book. The author has used two production-ready Python frameworks that are TensorFlow and Scikit-Learn. With thorough examples but minimal theory to impart the knowledge of deep learning.
Fundamentals of Machine LearningThe Machine Learning LandscapeEnd-to-End Machine Learning ProjectClassificationTraining ModelsSupport Vector MachinesDecision TreesEnsemble Learning and Random ForestsDimensionality ReductionNeural Networks Deep LearningUp and Running with TensorFlowIntroduction to Artificial Neural NetworksTraining Deep Neural NetsDistributing TensorFlow Across Devices and ServersConvolutional Neural NetworksRecurrent Neural NetworksAutoencodersReinforcement learning
Aurélien Géron is a consultant for machine learning. He is a former Googler and leader of YouTube’s video classification team. He has worked in different domains like Finance, Defense, and Health Care as a software engineer.
Author: David FosterFormat: Kindle/PaperbackPublisher: O’Reilly MediaPages: 330Edition: 1st
Artificial Intelligence has made it possible that a machine can be taught to paint, write, and compose music. This book will teach machine learning engineers and data scientists to recreate generative deep learning models like auto-encoders, encoder-decoder models, world models, etc.
Generative ModellingDeep LearningVariational AutoencodersGenerative Adversarial NetworksPaintWriteComposePlayFuture of Generative ModellingConclusion
David Foster is the co-founder of a data science consultancy named Applied Data Science. He is a winner of different international machine learning competitions and has won first prize for visualization to optimize site selection for the sake of clinical trials for a pharmaceutical company in the US. He is a master’s in mathematics and Operational Research from Trinity College, Cambridge, and the University of Warwick respectively.
Author: Leonard EddisonFormat: Audio Book/PaperbackPublisher: CreateSpace Independent Publishing PlatformPages: 292Edition: 1st
This book is designed especially for beginners and encapsulates the basics and importance of machine learning. It also focuses on different branches of machine learning and their applications in a wider spectrum. This book enables its readers’ coding in Python.
Basics of AIDecision TreesDeep Neural NetworksBasics of Python Programming LanguageLogistic Regression
Leonard Eddison is a blogger and teacher of Computer Science. He has written many books. He was born in Buffalo, NY. He passionately writes books to transfer his knowledge to them to pass it on to others.
Author: Sebastian RaschkaFormat: Kindle/PaperbackPublisher: Ingram Short TitlePages: 454Edition: 1st
Readers can access the world of predictive analysis with the help of this book. It teaches the practices and methods for the improvisation and optimization of machine learning systems and algorithms.
Giving Computers the Ability to Learn from DataTraining Machine Learning Algorithms for ClassificationA Tour of Machine Learning Classifiers Using Scikit-LearnBuilding Good Training Sets – Data ProcessingCompressing Data via Dimensionality ReductionLearn Best Practices for Model Evaluation and Hyperparameter TuningCombining Different Models for Ensemble LearningApplying Machine Learning to Sentiment AnalysisEmbedding ML Model into a web applicationPredicting Continuous Target Variables with Regression AnalysisWorking with Unlabeled Data – Clustering AnalysisTraining Artificial Neural Networks for Image RecognitionParallelizing Neural Network Training with Theano
Sebastian Raschka is a student at Michigan State University, Pursuing his Ph.D. He has been ranked by GitHub as the most influential data scientist. He is regularly contributing to the methods he implemented to open-source projects.
18- Data Mining
Author: Ian H. Witten, Eibe Frank, Mark A. HallFormat: Kindle/PaperbackPublisher: Morgan KaufmannPages: 654Edition: 4th
This book gives a thorough knowledge of the concepts of ML and the application of tools and techniques in the mining situation of real-world data.
ClusteringComparing Data Mining MethodsKnowledge representation & ClustersLinear ModelsPredicting performanceStatistical ModellingTraditional and Modern data mining techniques
Ian H. Witten is a Chartered Engineer at the Institute of Electrical Engineers London. He is a computer scientist at Waikato University, New Zealand. Eibe Frank is a computer scientist and developer of the WEKA machine. Mark Hall is a Data Scientist at Pyramid Analytics
Author: Nishant ShuklaFormat: Paperback/E-bookPublisher: Manning PublicationsPages: 272Edition: 1st
This book describes the ML basics with clustering, prediction algorithms, and traditional classification. Its deep learning concepts makes the reader qualified for ML task by using open-source, free TensorFlow library.
AutoencodersConvolutional, recurrent, reinforcement neural networksDeep learningHidden Markov modelsLinear regressionReinforcement learning
Nishant Shukla is a researcher in computer vision models focusing on ML techniques in robotics.
Author: Andreas C. Muller, Sarah GuidoFormat: Kindle/PaperBackPublisher: O’Reilly MediaPages: 392Edition: 1st
This book teaches practical methods of building ML solutions. It teaches important steps for constructing ML applications using Scikit-Learn and Python.
Advanced methods for model evaluation and parameter tuningApplications, fundamental concepts of MLML algorithmsMethods for working with text dataPipelines for chaining models and encapsulating workflowRepresentation of processed data.
Andreas C. Muller acquired his Ph.D. in ML from the University of Bonn. He worked in Amazon as an ML researcher on computer vision applications. Sarah Guido works at Reonomy as a data scientist.
We have compiled a detailed and extensive list of Machine Learning books. These will deliver detailed information on ML for beginners as well as experts. There are other resources also available to expand one’s knowledge of ML. These books tell us that ML is the way forward for IT novices and experts.
List of some other machine learning books
Advances in Financial Machine Learning ( by Marcos Lopez de Prado )Machine Learning: An Algorithmic Perspective (by Stephen Marsland)Deep Learning (Adaptive Computation and Machine Learning series)Think Stats – Probability, and Statistics for Programmers by Allan B. DowneyNeural Networks and Deep Learning (by Pat Nakamoto)The Hundred-Page Machine Learning BookAI and Machine Learning for Coders: A Programmer’s Guide to Artificial IntelligenceMachine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOpsMathematics for Machine Learning